The key innovation of this project is the development of robust methods for the integration of radar-based EO data into current and emerging systems for monitoring the impact of fire on vegetation. Rapid fire extent mapping, including fire progression of large wildfires, will be based on dense time-series of synthetic aperture radar (SAR), optical data and machine learning. The research will also explore the capabilities of SAR and LiDAR data, integrated with optical data, for distinguishing structural characteristics of post-fire recovery dynamics.

The overarching focus of the research is on the integration of multi-sensor EO data to fill key gaps in operational monitoring of the impacts of wildfire. The project aims to support land and fire managers to make more informed decisions, by developing more accurate and timely measures of burnt area extent and tools for monitoring post-fire recovery.

Flooding is a common and extremely impactful event within Australia and around the world. For example, the March 2021 Australian floods are a series of floods that began from 18 March 2021 which have affected New South Wales, from the North Coast to the Sydney metropolitan area in the south, in a disaster described as a “one-in-100-year event”. Additionally, far-south and far-southeast communities in Queensland were also greatly affected by flooding and heavy rainfall.

The aim of this project is to develop and operationalise smart analysis of SAR and optical satellite imagery (primarily NovaSAR and Sentinel missions) to address time-critical applications such as flood mapping (2D) and floodplain water harvesting (3D), based on many years of research in this area by the project team since 2009. Project activities include feasibility studies, remote sensing software
development (analytic toolbox) and extensive case studies.

The expected outcomes are:

1. A suite of near real-time, cross platform, scalable and operational tools for mapping floods with satellite remote sensing, ready for flood management agencies to takeover and/or private sector to commercialise, and improve volume estimate during the floodplain harvesting event for the Murray-Darling Basin states;
2. A comprehensive report on feasibility studies to inform a Phase 2 project; and
3. A comprehensive report on the case studies, targeting a range of users and promoting SmartSat CRC research through the mass media. The project brings together core partners such as UNSW, NSW Department of Planning and Environment, and Geoplex / Nova Systems, an ideal mix of academia, end user and geospatial service provider. The proposed project has also attracted strong support from the Federal Department of Agriculture, Water and Environment (DAWE) because of its significant national benefits (Letter of Support attached), as well as other key players such as Airbus and Geospatial Intelligence Pty Ltd.

The aim of this research is to improve and advance upon signal processing algorithms and functions developed for geostationary satellites. The results obtained will be used to improve the positional detection and tracking of fire, and its spatial characterisation, to inform emergency services where to allocate and deploy fire combat resources. This final output is aligned with SmartSat’s “Disaster and Emergency Management” application area.

The proposed research project “Monitoring changes in water quality in response to landcover disturbance with Earth Observations in Australia” aims to develop the methods to link in situ and satellite information to monitoring transport processes and recovery of aquatic ecosystems, namely inland waterbodies, after landcover disturbance events to further understand causal relationships for the purposes of reducing the impact on protected water bodies against future natural disasters for Australia.

The aim of this project is to research and develop an automated technical solution that solves the problem of integrating large commercial Earth Observation data workflows into open source and/or academic projects as demonstrated by our partner CSIRO – Open Data Cube (ODC). This will result in unprecedented industry efficiency and standardisation, leading to high frequency automated processing of large high resolution EO data types.

Data rich high resolution satellite Earth observation data contains enormous analysis potential. However, acquiring this data through traditional methods presents users with numerous hurdles when integrating into user workflows. Large file sizes, disparate data archives, lack of standardisation, poor workflow integration option, opaque pricing and licensing terms stunt the uptake and unlocking of valuable insights from these datasets. This project addresses these issues directly, creates new opportunities for users and unlocks new demand for data suppliers.

This project aims to develop a machine learning (ML) model that fuses satellite imagery and in-situ data for water monitoring and analysis for the benefit of water and environmental management. The result of this study will enable national monitoring of water bodies by using data fusion from satellite images and in-situ data; thus, limiting the risk of bush fires damaging ground-based water monitoring infrastructure.

This research aligns well with the AquaWatch Demonstrator as it develops artificial intelligence (AI) algorithm that will provide accurate forecasting of water degradation in Australia. The AI will map major environmental events such as algae blooms, bushfires, and other meteorological events to the degradation of Australian reservoirs, waterways and coastal environments. The proposed models will combine space and ground segments into an integrated metric for both near real-time decision-making as well as long term analysis and future forecasting.